Filipe Rodrigues


Can Topic Modelling benefit from Word Sense Information?
Adriana Ferrugento | Hugo Gonçalo Oliveira | Ana Alves | Filipe Rodrigues
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper proposes a new topic model that exploits word sense information in order to discover less redundant and more informative topics. Word sense information is obtained from WordNet and the discovered topics are groups of synsets, instead of mere surface words. A key feature is that all the known senses of a word are considered, with their probabilities. Alternative configurations of the model are described and compared to each other and to LDA, the most popular topic model. However, the obtained results suggest that there are no benefits of enriching LDA with word sense information.


ASAP: Automatic Semantic Alignment for Phrases
Ana Alves | Adriana Ferrugento | Mariana Lourenço | Filipe Rodrigues
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)